Advancing a community-led zebrafish toxicology phenotype atlas

Zebrafish have greatly advanced toxicology and environmental studies as a valuable animal model because they are easy to manipulate, breed, and observe during development. However, the absence of universal standards significantly impedes scientific progress and therefore human and environmental health. Standard ontologies and data models are acutely needed to improve data compatibility within and across species and domains. We propose to address these needs by creating community-built standards for annotating zebrafish toxicological exposure and their phenotypic outcomes (toxicophenotypes). We will design and deploy a toxicophenotype data model that will advance ontologies such as the Zebrafish Phenotype Ontology (ZP). This data model will allow integration and interoperability across species and across toxicological studies. We will create a toxicophenotype annotation toolkit that will allow users to annotate their data conforming to the newly created toxicophenotype data model and ZP, and therefore create “born-interoperable” data. Finally, we will instantiate a zebrafish toxicophenotype atlas web application. This atlas will serve as a visual definition of the standards and their documentation for examining variations of specific phenotypes by laboratories in the community. Users will be able to explore and query exposures and phenotypes of interest and see example images demonstrating the phenotypes. This project will community-governed by diverse stakeholders in toxicology and environmental health sciences to ensure fit-for-purpose design and sustainability. Towards that end, we will create robust, transparent structures for deciding on standards content, structure, and versioning; attribution policies; and content management and access practices. We will coordinate requirements gathering, documenting best practices for using the standards and annotations, usability testing, and plans for sustainability. Realizing interoperable toxicophenotypic data is crucial to improve data integration across scale and granularity; thereby accelerating an understanding of environmental influences on health.